Latest AI and machine learning research in strokes for healthcare professionals.
Background: Frailty is common in acute ischemic stroke (AIS) and predicts poor outcomes, but is not routinely captured in acute stroke care. Manual frailty tools are difficult to apply consistently in busy inpatient settings, while existing electronic frailty indices (eFIs) often rely on limited data modalities. We developed a scalable pre-stroke electronic frailty index (eFI) using multisource el...
Purpose: EEG-based brain-machine interfaces (BMIs) may support assistive technologies for individuals with stroke-related motor impairment by translating neural activity into control commands for external devices. However, post-stroke neural reorganization and interindividual EEG variability challenge reliable decoding. This study characterized motor imagery EEG features in healthy and acute strok...
Large language models (LLMs) are increasingly used for automated quality control (QC) of radiology reports. However, the reliability of LLMs on report...
Background Hypertension remains one of the most challenging healthcare problems in the community. It is a common, measurable, and treatable condition ...
Importance: Machine-learning models for ischemic stroke risk prediction are rarely validated across ancestrally distinct cohorts, and the contribution...
Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. De...
Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed. Currently, this assessment is a rate-limit...
Acute ischemic stroke (AIS) is a leading cause of disability and death while effective treatment requires quick and accurate diagnosis. Non-contrast C...
Background: Corticospinal tract (CST) damage is a major cause of post-stroke motor deficits. However, it remains unclear which estimates of CST damage...
Background: Prognostication in large vessel occlusion (LVO) stroke remains challenging. Although several prognostic models exist, their comparison to ...
Restoring hand function remains a major challenge for individuals with motor impairments following stroke. Noninvasive brain-computer interfaces (BCIs...
Self-supervised DINO models provide strong transferable visual representations, yet applying them directly to image segmentation remains challenging. ...
Objective: To construct and evaluate, in an exploratory manner, a pathophysiologic rationale link- ing biological pathways derived from the peripheral...
We built a multi-task pipeline for tennis stroke biomechanics from plain RGB video. On top of pose-based stroke recognition, it adds two new tasks, pr...
Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimat...
Background: Machine learning models for stroke mortality prediction typically treat each time horizon independently and use flat tabular features that...
Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on...
Accurate segmentation of carotid arteries in ultrasound imaging is critical for stroke risk assessment. However, speckle noise, low contrast, and blur...
Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on...
Background: In atrial fibrillation (AF), cerebral microbleed (CMB) burden guides anticoagulation decisions, yet AF is itself inconsistently associated...